Dual burden of infectious and chronic disease in low-resource U.S. communities: examining relationships between infection, adiposity, and inflammation
Bibliographic record
Abstract
Background Rising global obesity rates are linked with inflammation and associated morbidities. These negative outcomes are generally more common in low-resource communities within high-income countries; however, it is unclear how frequent infectious disease exposures in these settings may influence the relationship between adiposity and inflammation.Aim We test associations between adiposity measures and distinct forms of inflammation among adults (n = 80) living in low-resource U.S. communities experiencing high levels of obesity and pathogen exposure.Subjects and methods Adiposity measures included BMI and percent body fat. Inflammation measures included systemic inflammation (C-reactive protein [CRP]) and localised intestinal inflammation (faecal calprotectin [FC]). The relationship between a condition characterised by elevated inflammation (Helicobacter pylori infection) and adiposity was also considered.Results Adiposity was not significantly related to FC concentration. However, both adiposity measures were positively related with odds of CRP elevation and H. pylori infection was associated with significantly lower adiposity measures (all p < 0.05).Conclusion For this disadvantaged U.S. sample, the association between adiposity and inflammation varies by the systemic/localised nature of inflammation and the likely underlying cause of inflammation. Defining these associations will improve understanding of how rising obesity rates shape long-term health inequities, with implications for more effective intervention design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".